Abstract
Abstract This study presents a machine learning approach for identifying encryption algorithms based solely on ciphertext analysis. The problem is formulated as a supervised classification task, where features are learned directly from ciphertext samples generated by four widely used symmetric encryption algorithms. Experimental results show that the model can reliably distinguish between ciphers under constrained conditions, particularly when initialization vectors (IVs) and key distributions remain consistent across samples. These findings indicate that ciphertexts retain subtle statistical patterns that can be exploited for algorithm identification, while also highlighting limitations in generalization under distribution shifts.